Agentic AI Solutions for Regulated Enterprise Workflows
ZAAX Agentic AI Solutions designs and builds AI agents and retrieval-augmented generation (RAG) systems that run inside real business processes such as claims, member support and document intelligence. A pilot takes 6–12 weeks and costs $15,000–$60,000; full platforms cost $50,000–$250,000+. Every build includes grounding, human-in-the-loop controls, logging and evaluation to reduce hallucinations in regulated settings.
Bespoke autonomous AI agents and hallucination-mitigated RAG architectures engineered for regulated industries to automate complex operational workflows — from insurance claims adjudication to fintech reconciliation and healthcare policy lookup.
| Key Fact | Detail |
|---|---|
| Best for | Enterprises with high-volume document workflows, regulated operators needing audit trails, teams replacing manual review with AI-augmented decisions |
| Typical duration | Pilot 6–12 weeks · Multi-agent platform 3–6 months · RAG system 8–16 weeks |
| Main deliverables | Agent architecture, RAG pipeline, evaluation harness, human-in-the-loop workflows, production monitoring |
| Pricing | Pilot $15,000–$60,000; Multi-agent/RAG platform $50,000–$250,000+; Retainer $8,000–$40,000/month |
| Domains | Insurance claims, healthcare, fintech compliance, legal review, enterprise knowledge |
Who This Is For
Enterprises with high-volume document workflows (claims, contracts, compliance)
Regulated operators needing audit trails and refusal controls
Teams replacing manual review with AI-augmented decisions
What You Get
Custom AI agent development
Autonomous process automation
AI API Integration (OpenAI, Claude, etc.)
RAG-powered knowledge systems
Hallucination-controlled chatbots
Evaluation harness for continuous accuracy testing
Our Methodology
Use-case framing & data audit
Identify high-value workflows; assess document quality, volume and compliance constraints
RAG pipeline design
Chunking, embedding, retrieval strategy, citation logic, refusal rules
Agent architecture
Tool definitions, planning loops, memory, guardrails, human handoff points
Evaluation harness
Golden sets, regression tests, accuracy/latency/cost dashboards
Pilot → scale
6–12 week pilot with defined success criteria; then phased rollout
Typical Timelines
| Stage | Duration |
|---|---|
| Pilot (single agent or RAG) | 6–12 weeks |
| Multi-agent platform | 3–6 months |
| RAG system (knowledge base) | 8–16 weeks |
| Ongoing retainer | Continuous improvement |
Technologies
OpenAI, Anthropic, Google models · LangChain, LangGraph, LlamaIndex · Vector databases (Pinecone, Weaviate, Qdrant) · Custom evaluation frameworks · Read-only system integrations · Observability (LangSmith, custom)
Pricing & Engagement Models
Start with a pilot to validate outcomes, then scale to a platform or retainer.
| Model | Best For | Typical Band |
|---|---|---|
| Pilot Engagement | Validate one workflow with measurable outcome | $15,000–$60,000 |
| Multi-Agent / RAG Platform | Production system with multiple agents | $50,000–$250,000+ |
| Retained AI Engineering (monthly) | Continuous improvement, new use cases | $8,000–$40,000/month |
| Model Usage Fees | API costs for LLM inference | Pass-through or included in retainer |
Pricing is indicative and varies with scope, compliance requirements and region.
When This Is — And Isn't — the Right Fit
Right Fit
- You have a repetitive, document-heavy workflow that experts spend hours on daily
- You need answers grounded in your own policies, not generic training data
- You require audit trails, citations and refusal controls for compliance
Not the Best Fit
- You only need a generic chatbot for FAQ — use an off-the-shelf tool
- You have no proprietary documents or workflows to ground the system in
- You cannot commit to an evaluation harness and ongoing monitoring
Related Case Studies
- Multi-Lingual AI Chatbot & Concierge
An enterprise-grade, RAG-driven AI Concierge delivering hallucination-mitigated policy and claim clarity through secure insurance system integration.
- AI Credit Card Auditor: Automated Expense Matching & Parsing
Reduces manual reconciliation effort by approximately 50 %, cuts dispute resolution time and provides a complete, auditable trail.
- Browse all AI delivery outcomes →
Explore case studies in insurance, healthcare and fintech
Frequently Asked Questions
What is agentic AI and how is it different from a chatbot?
Agentic AI refers to systems that plan and carry out multi-step tasks using tools and company data, rather than only answering questions. A chatbot replies; an agent can, for example, look up a claim, check a policy rule and prepare a response for human approval.
What is a RAG system and why do enterprises need one?
Retrieval-augmented generation (RAG) lets a language model answer from your own approved documents instead of relying only on its training. This keeps answers grounded in current policy or product information and allows the system to cite its sources or refuse when it does not know.
How do you reduce AI hallucinations in regulated industries?
ZAAX combines grounded retrieval, refusal rules when evidence is missing, read-only access to core systems where required, human review for sensitive actions, and an evaluation harness that tests accuracy continuously. No system can promise zero errors, so controls and monitoring are designed in.
How much does an enterprise AI agent cost and how long does it take?
A pilot typically takes 6–12 weeks and costs $15,000–$60,000. A multi-agent or RAG platform costs $50,000–$250,000+ and takes 3–6 months to roll out. Model usage fees are either passed through or included in a retainer.
Ready to Move from AI Demos to Production Agents?
ZAAX builds hallucination-controlled RAG systems and autonomous agents that integrate with your core operations — not just your Slack.
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